There are approximately 250,000 new cases of brain tumors diagnosed per year in the US. These lesions represent some of the most difficult diagnoses with regards to management and frequently, if not always, require a combination of surgical and chemotherapeutic and/or radiation treatment. The central nervous system is densely organized into multiple eloquent cortical areas responsible for language, vision, motor, and other important functions. White matter (WM) pathways such as the arcuate fasciculus (AF), optic radiations (OR), and cortico-spinal tract (CST) form the complex and communication structure between these cortical areas leading to key eloquent brain disrupt function.
During brain surgery, a neurosurgeon may need to cut or push brain fiber tracts, the neuronal cables that connect the critical brain areas, in order to reach a mass or tumor. The goal of modern treatment planning is to determine the optimal resection margin that maximizes tumor removal while preserving language, visual and motor function. With mounting evidence that the extent of resection (EoR) of a brain tumor is linked to survival, maximizing the EoR while preserving function is of critical importance.
Surgical planning relies heavily on MRI to visualize anatomic structures with high fidelity. Since its introduction, diffusion magnetic resonance imaging (dMRI) (both diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI) has provided critical insight into the WM fiber pathways of the brain. Specifically, DTI ushered in a new era in surgical planning by enabling the visualization of fiber tracts and characterization of WM changes. The direction and magnitude of water diffusion is encoded and modeled by dMRI and subsequently used by fiber tracking algorithms reconstruct WM pathways of interest. Clinical neuro-navigation systems today provide fiber reconstruction (typically DTI-based) to visualize the anatomic relationship between a specific WM pathway, e.g., CST and the lesion of interest, e.g., tumor, and are becoming the standard of care for treating lesions in vicinity of eloquent brain. Further, Current software tools are validated only on the main trunk of the CST. Identifying eloquent tracts in the vicinity of a tumor requires the ability to perform fiber tracking through regions affected by edema and mass effect. Edema, which manifests as a change in the free water (FW) content of tissue, is a significant confounder for surgical planning as it lowers anisotropy and obfuscates the presence of the underlying fiber bundle, which may remain completely unaffected.
There is now a growing use of fiber tractography for surgical planning. In today's clinical environments, tractography is one of the most complicated and time-consuming processes. This process, of course, needs to be repeated for every tract. Current solutions require the placement of multiple inclusion and exclusion (filtering) ROIs to extract the tract of interest. Finally, the end result is “tweaked” by the user through adjustments of FA threshold and angular curvature. This process, of course, needs to be repeated for every tract. Not only are the current tools to perform these tasks complex, but they are also unable to process higher order diffusion data, do not correct for mass effect or edema, or provide an interface to view/extract quantitative tract data, e.g., average FA, fiber count, etc. Furthermore, current tractography suites lack sufficient transparency for clinical research. When the methods and parameters used to obtain results are unknown, comparison of results between packages is meaningless and validation of one software suite, e.g., STIM, does not translate to another software package.
Unfortunately, the widespread use of tractography in neuro-oncology is hindered by several factors. From a clinical and practical standpoint, tractography requires expensive, proprietary software and a comprehensive knowledge of neuro-anatomy to place ROIs to initiate tracking, which introduces intra- and inter-user variability. This variability is further compounded by mass effect and the dependence of current algorithms on measures that prevent tracking through edema. Importantly, tractography software does not provide measures for immediate tract assessment.
Current tools frequently fail to reconstruct fiber pathways that are edematous, displaced or infiltrated by a surrounding tumor and in normal tissue with crossing fibers. Thus they treat edematous regions heuristically and cannot track through complex regions with crossing fibers, thus leading to artifactual and inadequate tracking results, thereby limiting clinical utility. Furthermore, these tools require manual placement of seed regions to initiate tracking, which is not only time consuming, but also subject to intra- and inter-user variability, differences in parameter selection as well as variability in software implementations. Finally, despite multi-modal imaging acquisitions, there is still a relative lack of robust structural and functional measures that quantitatively evaluate the change in WM tract characteristics in the presence of tumor and following resection.
Of the challenges identified above, a critical one that confounds all current clinical tools is the presence of edema in peritumoral regions where tracts may be intact but appear disrupted due to the presence of edema. This causes a change in DTI parameters, such as FA, leading to an artificial loss of fibers. Also, DTI-based tracking is inaccurate in WM regions with crossing fibers. Also, existing tractography algorithms use a plethora of mathematical algorithms, parameters and heuristics leading to variation in results, with no established standards for evaluating the differences.
Thus, there remains a need in the art for a validated treatment planning package that includes the following modules: 1) tractography that is robust to edema and mass effect, 2) automated extraction of complete and partial tracts confounded by edema and displaced by mass effect, and 3) robust quantification of WM tract health in the presence of tumor.